Protein Tau’s Role in Gene Expression

Group 25: Ana Moral (s232119), Jacqueline Printz (s194377), Jenni Kinnunen (s204697), João Prazeres (s243036), William Gunns (s242051)

1. Introduction - Protein Tau

  • Function: Microtubule protein essential for cytoskeletal stability and neuronal transport.

  • Supports healthy neuronal functions.

  • Destabilization linked to neuronal dysfunction, and Alzheimer’s Disease.

  • Previous studies concluded that Tau destabilization led to an alteration in the expression of glutamatergic genes.

Experimental Objective:

Is the overexpression of Tau associated to gene expression alterations?

Tau Protein Diagram

2. Experimental Setup

Differential gene expression analysis of RNA-seq data performed on:

  • Control: 3 samples of SH-SY5Y cells with overexpression of a control vector.
  • Experimental Condition: 3 samples of SH-SY5Y cells with overexpression of Tau 0N4R isoform.

RNA-seq data was reported on 3 xls sheets:

  • Read Counts.
  • RPM (Reads Per Million).
  • RPKM (Reads Per Kilobase Million).

The 3 sheets were joined into one large tibble data frame.

# A tibble: 58,395 × 9
   ...1     GeneName description SH_ctrl_1 SH_ctrl_2 SH_ctrl_3 SH_tau_1 SH_tau_2
   <chr>    <chr>    <chr>           <dbl>     <dbl>     <dbl>    <dbl>    <dbl>
 1 ENSG000… TSPAN6   tetraspani…       319       582       280      214      189
 2 ENSG000… TNMD     tenomoduli…         0         0         0        0        0
 3 ENSG000… DPM1     dolichyl-p…       792      1556       781      521      502
 4 ENSG000… SCYL3    SCY1 like …       517       561       445      323      365
 5 ENSG000… C1orf112 chromosome…       533       537       566      601      584
 6 ENSG000… FGR      FGR proto-…         0         0         0        2        2
 7 ENSG000… CFH      complement…         2         0         1        0        0
 8 ENSG000… FUCA2    alpha-L-fu…       487       761       447      341      321
 9 ENSG000… GCLC     glutamate-…       430       703       246      233      218
10 ENSG000… NFYA     nuclear tr…      1101      1156       760      898      583
# ℹ 58,385 more rows
# ℹ 1 more variable: SH_tau_3 <dbl>

3. Data Wrangling

First the data was prepared and made clean by, 1. by joining all dataframes into one 2. renaming columns 3. removing unecessary and invalid observations (including descriptions and rows of data that are all zero)

First the data was prepared and made clean by:
1. by joining all dataframes into one
2. renaming columns
3. removing unecessary and invalid observations (including descriptions and rows of data that are all zero)

Some of the key functions that were used include full_join, mutate, rename, select) Following this, each row corresponds to an observation, each column corresponds to a variable and each cell is a value. Could insert picture of ‘clean’ data - lecture style.

The data was then log transformed to enable a deeper analysis of observations with small margins of difference. Average across trials of experiment (and variance) THINK THIS BIT IS FOR AUGMENT

4. Data Augment

Normalized Data

  • Log transformation applied to selected columns
  • Small value (0.0001) added to avoid zeros in data

Calculated Mean

  • Mean values for control and tau groups were calculated across replicates

Fold Change

  • Calculated fold change between control and tau groups for each measure
  • Fold change values used to filter significant genes

Filtered Data

  • Genes with significant fold change (>1 or <-1) retained
  • Replicates averaged for non-significant genes

Final Data

  • Data stored in three separate files for analysis

Description

5. Data Description part 1

  • All data: all genes, mean of replicates
  • Filtered data: significant genes, mean of replicates (for overview of the data)
  • Log data: significant genes, all 3 replicates (for PCA)
# A tibble: 3 × 3
  Data          Genes Attributes
  <chr>         <dbl>      <dbl>
1 All data      19279         10
2 Filtered data  2036         10
3 Log data       2036         19

6. Data Description part 2

7. Analysis PCA

Objective

  • Confirm that RPM, RPKM, and reads yield similar results.
  • Verify differences between control and tau experiments.

Approach

  • 3 PCAs performed separately for RPM, RPKM, and reads data.
  • 1 final PCA conducted on combined data.

Results

  • Plots of individual PCAs show how each data type clusters.
  • Final PCA confirms global differences between control and tau groups.

test

8. Analysis PCA

9. Gene Set Enrichment Analysis (GSEA)

Computational method to determine if a set of genes shows statistically significant differences in control and Tau over expressing conditions.

::: {.left} - Plot genes

::: {style=“width: 45%; text-align: left;”} Over and under expressed genes

::: {.right} - Plot pathways

::: {style=“width: 45%; text-align: right;”} Over and under expressed pathways ————————————————————————

10. Discussion based on the GSEA/conclusion

Gamma and Alpha response- cytokine, regulation of immune response, trigger JAK-STAT3 pathway. JAK-STAT3 pathway- immunity, cell division, and other cellular processes.

–Promote neuroinflammation in neurodegenerative disorders like Alzheimer’s by initiation innate immunity, adaptive immune mechanisms, and neuroinflammatory response. (https://pubmed.ncbi.nlm.nih.gov/36614305/#:~:text=The%20JAK%2FSTAT%20signaling%20pathway%20is%20one%20of%20the%20critical,and%20finally%2C%20constraining%20neuroinflammatory%20response.)

-Supports: link of Tau overexpression to neurodegenerative disorders like Alzheimer’s.

Challenges

Data handling and cleaning: setting arbitrary boundaries without losing too much data or leaving in unnecessary data.